Quarto is a stunning tool. It transforms your R or Python code in a very clean report that includes code, charts, explanation and everything else you would like to include.
Lumo is a quarto template. It slightly modifies the default appearance of a Quarto document, to make it even more shiny, and fit your brand.
This document demoes how a document made with lumo looks like, list its main features and explain how to use it in 3 minutes.
Date: September 10, 2024
Quarto is probably my favorite tool in the R/Python universe. Transforming your code and thoughts into a stunning report in one click always blows my mind.
Even if the default appearance looks good, there are a few tweaks I always like to apply to push the report to the next level.
Btw, you can learn how to master Quarto thanks to my online course: Productive R Workflow
This document aims at showcasing how a quarto document made with the lumo format looks like.
library(tidyverse)
library(hrbrthemes)
library(viridis)
library(ggiraph)
library(patchwork)
library(sf)ggplot2 theme: theme_ipsum()
Don’t forget that you can add full width content, which can be very useful for instance for a nice interactive map made with leaflet.
# Load the library.
library(leaflet)
# Make a map
leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng = 174.768, lat = -36.852, popup = "The birthplace of R")All right, many people know how to create interactive graphs with plotly. But did you know that you can connect graphs together thanks to ggiraph? And using a syntax pretty close from ggplot2?
Pretty close from dark magic isn’t.
# Read the full world map
world_sf <- read_sf("https://raw.githubusercontent.com/holtzy/R-graph-gallery/master/DATA/world.geojson")
world_sf <- world_sf %>%
filter(!name %in% c("Antarctica", "Greenland"))
# Create a sample dataset
happiness_data <- data.frame(
Country = c(
"France", "Germany", "United Kingdom",
"Japan", "China", "Vietnam",
"United States of America", "Canada", "Mexico"
),
Continent = c(
"Europe", "Europe", "Europe",
"Asia", "Asia", "Asia",
"North America", "North America", "North America"
),
Happiness_Score = rnorm(mean = 30, sd = 20, n = 9),
GDP_per_capita = rnorm(mean = 30, sd = 20, n = 9),
Social_support = rnorm(mean = 30, sd = 20, n = 9),
Healthy_life_expectancy = rnorm(mean = 30, sd = 20, n = 9)
)
# Join the happiness data with the full world map
world_sf <- world_sf %>%
left_join(happiness_data, by = c("name" = "Country"))
# Create the first chart (Scatter plot)
p1 <- ggplot(world_sf, aes(
GDP_per_capita,
Happiness_Score,
tooltip = name,
data_id = Continent,
color = Continent
)) +
geom_point_interactive(data = filter(world_sf, !is.na(Happiness_Score)), size = 4) +
theme_minimal() +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
# Create the second chart (Bar plot)
p2 <- ggplot(world_sf, aes(
x = reorder(name, Happiness_Score),
y = Happiness_Score,
tooltip = name,
data_id = Continent,
fill = Continent
)) +
geom_col_interactive(data = filter(world_sf, !is.na(Happiness_Score))) +
coord_flip() +
theme_minimal() +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
# Create the third chart (choropleth)
p3 <- ggplot() +
geom_sf(data = world_sf, fill = "lightgrey", color = "lightgrey") +
geom_sf_interactive(
data = filter(world_sf, !is.na(Happiness_Score)),
aes(fill = Continent, tooltip = name, data_id = Continent)
) +
coord_sf(crs = st_crs(3857)) +
theme_void() +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none"
)
# Combine the plots
combined_plot <- (p1 + p2) / p3 + plot_layout(heights = c(1, 2))
# Create the interactive plot
interactive_plot <- girafe(ggobj = combined_plot)
interactive_plot <- girafe_options(
interactive_plot,
opts_hover(css = "fill:red;stroke:black;")
)
interactive_plotFigure: three graphs connected thanks to ggiraph
It is very easy to insert an interactive table in your document thanks to the DT package. The output allows to filter rows, search for something and sort using a specific columns!
Install the library with install.packages("DT"). Then, just pass a dataframe to the datatable() function to get a stunning interactive output!
library(DT)
data(iris)
# Make a table
datatable(iris, filter = "top")It’s always good to have a grey section. Makes the document breath a bit.
Let’s use this space to render a little equation:
\[ x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} \]
You can learn more about controlling the appearance of HTML output here: https://quarto.org/docs/output-formats/html-basics.html
If you have several plots to make, each targeting something special in your dataset, you should consider using pills for that!
Always a good scatterplot
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) +
geom_point() +
theme_ipsum()
You can also make a boxplot, but please do not hide the underlying distribution! This is widely explain in my data-to-viz.com project in case you’re not convinced 😋.
# create a dataset
data <- data.frame(
name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100) ),
value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )
)
# Plot
data %>%
ggplot( aes(x=name, y=value, fill=name)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
Content that will show under first tab
data %>%
ggplot( aes(x=name, y=value, fill=name)) +
geom_boxplot() +
scale_fill_viridis(discrete = TRUE, alpha=0.6) +
geom_jitter(color="black", size=0.4, alpha=0.9) +
theme_ipsum() +
theme(
legend.position="none",
plot.title = element_text(size=11)
) +
ggtitle("A boxplot with jitter") +
xlab("")
When you make a Quarto doc, it is a good practice to include information about your working environment. This is straightforward, you just have to call the sessionInfo() info and all your R and package versions will be printed. This is a huge boost for reproducibility.
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Paris
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DT_0.33 leaflet_2.2.2 sf_1.0-16 patchwork_1.2.0
[5] ggiraph_0.8.10 viridis_0.6.5 viridisLite_0.4.2 hrbrthemes_0.8.7
[9] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[13] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[17] ggplot2_3.5.1 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.5 bslib_0.7.0 xfun_0.43
[4] htmlwidgets_1.6.4 tzdb_0.4.0 crosstalk_1.2.1
[7] vctrs_0.6.5 tools_4.4.0 generics_0.1.3
[10] curl_5.2.1 proxy_0.4-27 fansi_1.0.6
[13] pkgconfig_2.0.3 KernSmooth_2.23-22 uuid_1.2-0
[16] lifecycle_1.0.4 farver_2.1.1 compiler_4.4.0
[19] munsell_0.5.1 httpuv_1.6.15 fontquiver_0.2.1
[22] fontLiberation_0.1.0 sass_0.4.9 class_7.3-22
[25] htmltools_0.5.8.1 yaml_2.3.8 Rttf2pt1_1.3.12
[28] jquerylib_0.1.4 pillar_1.9.0 later_1.3.2
[31] crayon_1.5.2 extrafontdb_1.0 gfonts_0.2.0
[34] classInt_0.4-10 cachem_1.0.8 mime_0.12
[37] fontBitstreamVera_0.1.1 tidyselect_1.2.1 digest_0.6.35
[40] stringi_1.8.4 labeling_0.4.3 extrafont_0.19
[43] fastmap_1.1.1 grid_4.4.0 colorspace_2.1-0
[46] cli_3.6.2 magrittr_2.0.3 crul_1.4.2
[49] utf8_1.2.4 e1071_1.7-14 withr_3.0.0
[52] gdtools_0.3.7 scales_1.3.0 promises_1.3.0
[55] timechange_0.3.0 rmarkdown_2.26 gridExtra_2.3
[58] hms_1.1.3 shiny_1.8.1.1 evaluate_0.23
[61] knitr_1.46 rlang_1.1.3 Rcpp_1.0.12
[64] DBI_1.2.2 xtable_1.8-4 glue_1.7.0
[67] httpcode_0.3.0 rstudioapi_0.16.0 jsonlite_1.8.8
[70] R6_2.5.1 units_0.8-5 systemfonts_1.0.6